Empowering research in chemistry and materials science through intelligent algorithms
Jinglong Lin, Fanyang Mo
Abstract
In this review, we delve into the burgeoning utilization of intelligent algorithms within the realms of chemistry and materials science. Starting with an elucidation of the fundamental tenets of Machine Learning (ML), Deep Learning (DL), and optimization algorithms, we examine their tailored fit to the unique exigencies of chemical materials. We accentuate the integral role of data collection, refinement, and feature engineering, providing methodologies for data extraction from specific databases and literature. A detailed exploration follows, encapsulating the distinct applications of these intelligent algorithms in chemistry and materials science. The review concludes with a forward-looking perspective on the emergent applications of these intelligent paradigms in chemistry, spurring future inquiries.